Classification of patients with a clinically isolated syndrome based on signs and symptoms is supported by magnetic resonance imaging results
Bibliographic record
Abstract
BACKGROUND: Recently, a clinical classification system was described to determine whether symptoms and signs of patients presenting with a first episode suggestive of multiple sclerosis (MS) indicate the presence of monofocal or multifocal disease. OBJECTIVES: To evaluate the value of this new classification system by comparing the results with those of simultaneously obtained magnetic resonance imaging (MRI) scans. METHODS: The 487 patients, randomised in the BENEFIT study, were centrally assessed using the new system and classified as monofocal or multifocal, based on clinical information by two neurologists masked for the MRI results. MRI analyses were performed by expert readers masked for the clinical classification. RESULTS: Patients classified as multifocal had more T2 hyperintense (median: 21 versus 15.5) and more T1 hypo-intense lesions (median: 2 versus 1) than those classified as monofocal. Patients classified at the local site as having evidence of a single clinical lesion, but reclassified centrally as having a clinical multifocal central nervous system presentation, had more T2 lesions than monofocal patients. In addition, patients with a multifocal presentation more often fulfilled the MRI criteria for dissemination in space, as incorporated in the International Panel (IP) diagnostic criteria for MS. CONCLUSION: These data provide justification for the recently proposed clinical classification system to be used in patients who present with a first episode suggestive of MS, in that ;multifocal', based on symptoms and signs, is associated with more lesions on MRI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".